Gierad Laput

As computing becomes increasingly embedded into the fabric of everyday life, systems that
understand people’s context of use are of paramount importance. Regardless of whether
the platform is a mobile device, a wearable, or part of the “Internet of Things", context offers
an implicit dimension that is vital in reducing interactive viscosity between tasks and increasing
the richness of human-computer interactions. Sensors are the primary interface for
bringing context awareness, but existing sensing approaches are often costly, obtrusive,
and special-purpose. Numerous approaches have been attempted and articulated, though
none have reached widespread use to date.
Motivated by this problem, my thesis work focuses on enhancing context-awareness
through ubiquitous and unobtrusive sensing, drawing upon machine learning to unlock a
wide range of applications. I attack this problem space on two fronts: 1) using wearables to
transform the human arm into a context-sensing springboard, and 2) transforming everyday
spaces into smart environments with general-purpose sensors. The systems that I built have
been deployed across long periods and multiple environments, the results of which show
the versatility, accuracy and potential for practical context sensing. By combining novel
sensing with machine learning, my work transforms raw signals into intelligent abstractions
that can power rich, context-sensitive applications, unleashing the potential of next-generation
computing platforms.